Cross-Modality Attack Boosted by Gradient-Evolutionary Multiform Optimization (XXXX 2024)

Code for XXXX 2024 paper ``Cross-Modality Attack Boosted by Gradient-Evolutionary Multiform Optimization (XXXX 2024)".

Paper

Supplemental Material

Requirements:

python 3.7
CUDA==10.1
Market1501 (will transform to CnMix), Sketch-ReID, SYSU and RegDB dataset.
faiss-gpu==1.6.0
Other necessary packages listed in requirements.txt

Preparing Data

Clone our repo

Market-1501(namely CnMix) (SYSU and RegDB are the same):

Download "Market-1501-v15.09.15.zip".
Create a new directory, rename it as "data".
Create a directory called "raw" under "data" and put "Market-1501-v15.09.15.zip" under it.
The processed dataset is provided in the link below, please refer to it.
To adapt different dataset formats to this code, we have provided conversion scripts. Please refer to CnMix_process.py, cross-modal_dataset_to_market_format.py, deal_SYSU_testset_ID.py, and testset_to_query.py.
There is a processed tar file in BaiduYun (Password: kwwu) with all needed files.

Preparing Models

Download re-ID models from BaiduYun (Password: k4np)

Run our code

See run.sh for more information.

If you find this code useful in your research, please consider citing:

@inproceedings{XXXXX,
  title={Cross-Modality Attack Boosted by Gradient-Evolutionary Multiform Optimization},
  author={XXXXXXXXXx},
  booktitle={XXX},
  volume={35},
  number={4},
  pages={3128--3135},
  year={2024}
}

Contact Me

Email: fmonkey625@gmail.com

finger-monkey/Muitiform_Attack